Forecasting arrivals and occupancy levels in an emergency department
Forecasting arrivals and occupancy levels in an emergency department
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DOI：
10.1016/j.orhc.2019.01.002
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发表时间：
2019-06-01
影响因子：
2.1
通讯作者：
Zhang, Xiaopei
中科院分区：
文献类型：
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作者：
Whitt, Ward;Zhang, Xiaopei
这是Whitt 和Zhang (2017) 的续集，其中我们根据2004 年7 月以色列海法拥有1000 个床位的大型Rambam 医院的公开数据，并结合Armony 等人的患者流量分析，开发了急诊科(ED) 的总体随机模型。 （2015）。在这里，我们根据最近的历史记录（所有患者之前的到达和离开时间）和有用的外生变量，重点预测未来的每日到达总数并实时预测每小时的入住率。对于到达预测，我们将数据集分为用于拟合模型的初始训练集和用于评估性能的最终测试集。通过使用 200 周而不是之前 25 周的数据，我们确定了 (i) 到达过程和停留时间分布的长期趋势，以及 (ii) 连续每日到达总数之间的依赖性，这是以前无法检测到的。从包括人工神经网络模型在内的多种预测方法中，我们发现具有外生（假期和温度）回归量的季节性自回归综合移动平均线 (SARIMAX) 时间序列模型是最有效的。然后，我们将之前的 ED 模型与到达预测相结合，为未来的 ED 占用水平创建实时预测器。 (C) 2019 Elsevier Ltd. 保留所有权利。
This is a sequel to Whitt and Zhang (2017), in which we developed an aggregate stochastic model of an emergency department (ED) based on the publicly available data from the large 1000-bed Rambam Hospital in Haifa, Israel, from 2004-7, associated with the patient flow analysis by Armony et al. (2015). Here we focus on forecasting future daily arrival totals and predicting hourly occupancy levels in real time, given recent history (previous arrival and departure times of all patients) and useful exogenous variables. For the arrival forecasting, we divide the dataset into an initial training set for fitting the models and a final test set to evaluate the performance. By using 200 weeks of data instead of the previous 25, we identify (i) long-term trends in both the arrival process and the length-of-stay distributions and (ii) dependence among successive daily arrival totals, which were undetectable before. From several forecasting methods, including artificial neural network models, we find that a seasonal autoregressive integrated moving average with exogenous (holiday and temperature) regressors (SARIMAX) time-series model is most effective. We then combine our previous ED model with the arrival prediction to create a real-time predictor for the future ED occupancy levels. (C) 2019 Elsevier Ltd. All rights reserved.